High-resolution model building with multistage full-waveform inversion for narrow-azimuth acquisition data

Abstract

Full-waveform inversion (FWI) is an attractive tool for high-resolution velocity-model building without a high-frequency assumption compared to conventional reflection tomography. However, there are two main challenges to the application of FWI on narrow-azimuth acquisition (NAZ) data: cycle skipping and acquisition footprints. Here, a multistage FWI is proposed to build a high-resolution model for NAZ data. It is well known that FWI may suffer from a cycle-skipping problem when the starting model is not close enough to the true solution. To mitigate this problem, we introduce dynamic-warping preconditioned full-waveform inversion (DWFWI) as the first stage of the velocity inversion. DWFWI iteratively preconditions the observed early arrivals through dynamic warping to avoid cycle skipping in the model, which allows large-scale background updates. The second stage of our workflow is the conventional FWI with image-guided smoothing (IGFWI). On top of DWFWI, more reflection events are included and inverted through the conventional L2-norm FWI, which can add more detailed updates to the velocity model with much higher resolution. Due to poor crossline sampling, there are strong acquisition footprints on FWI updates in the crossline direction. Image-guided smoothing is used to reduce the acquisition footprints in the FWI gradient as well as to preserve the detailed update around the faults. It is then followed by an additional tomography to update the deep portion. This approach was applied on the Hoop Fault Complex data in the southwestern Barents Sea. The results show that a more geologically realistic and higher-resolution model of the Hoop Fault Complex was obtained, and the migration image and gathers were significantly improved.

High-resolution multistage FWI and image-guided tomography to resolve ultraslow gas anomalies

“…However, it also suffers from instability and inaccuracy if the proper FWI approach is not applied at the appropriate time in the model building process. Mao et al (2016) present a multistage FWI workflow which helps mitigate some of these issues. With this innovative method, early FWI passes use refracted diving wave data, as is common throughout the industry, but the data is dynamically warped (Ma and Hale, 2013) to simplify event-matching.…”

2D Near‐Surface Full‐Waveform Tomography Reveals Bedrock Controls on Critical Zone Architecture

“…Resolution is enhanced because FWT leverages more of the seismic waveform than other methods of seismic tomography (e.g., Fichtner, 2010; Schuster, 2017). As a result, FWT has been widely applied in global and exploration seismology (e.g., Choi & Alkhalifah, 2012; Lei et al., 2020; Mao et al., 2016; Pratt, 1999; Virieux & Operto, 2009). To date, however, FWT has only been used to study the near surface on a handful of occasions (e.g., Cai & Zelt, 2022; J. Chen et al., 2017; Köhn et al., 2018; X. Liu et al., 2022; Sheng et al., 2006; W. Wang, Chen, Keifer, et al., 2019; W. Wang, Chen, Lee, & Mu, 2019; Y. Wang, Miller, et al., 2019).…”

“…Another hurdle in applying FWT is the need for domain‐specific inversion strategies. For example, workflows used for inverting global seismology data, land seismic data, and marine reflection data all vary greatly (e.g., Borisov et al., 2020; Lei et al., 2020; Mao et al., 2016). FWT in the near surface is also challenging because of the strong velocity contrasts and heterogeneity in elastic properties due to the rapid compaction of regolith (e.g., Köhn et al., 2018; X. Liu et al., 2022; Sheng et al., 2006).…”

Improving Seismic Image With High Resolution Velocity Model From AWI Starting With 1D Initial Model - Case Study Barents Sea

“…The method was applied to overcome velocity-estimation challenges in the overburden that had led to a distorted seismic image. Published case studies have proven the versatility of FWI in resolving small-scale velocity features, in particular in the shallow parts of the overburden, where reflection-based velocity estimation techniques tend to struggle (Mao et al, 2016, Jones et al 2013. For a successful application of FWI, it is commonly assumed necessary to use a reliable starting seismic velocity model, based on well data and/or on pre-stack depth-migration reflection tomography, to overcome cycle skipping (Korsmo et al, 2016).…”